Automatic 3-dimensional quantification of orthodontically induced root resorption in cone-beam computed tomography images based on deep learning.

Journal: American journal of orthodontics and dentofacial orthopedics : official publication of the American Association of Orthodontists, its constituent societies, and the American Board of Orthodontics
PMID: 39503671

Abstract

INTRODUCTION: Orthodontically induced root resorption (OIRR) is a common and undesirable consequence of orthodontic treatment. Traditionally, studies employ manual methods to conduct 3-dimensional quantitative analysis of OIRR via cone-beam computed tomography (CBCT), which is often subjective and time-consuming. With advancements in computer technology, deep learning-based approaches have gained traction in medical image processing. This study presents a deep learning-based model for the fully automatic extraction of root volume information and the localization of root resorption from CBCT images.

Authors

  • Qianhan Zheng
    Department of Orthodontics, Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Hangzhou, PR China.
  • Lei Ma
    School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, Sichuan, China. Electronic address: leima@wit.edu.cn.
  • Yongjia Wu
    Department of Orthodontics, Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Hangzhou, PR China. Electronic address: yogurtwu@zju.edu.cn.
  • Yu Gao
    Department of Radiology Center, The First Affiliated Hospital of Xinxiang Medical University, Xin Xiang, China.
  • Huimin Li
    a Department of Pharmacy , Special Drugs R&D Center of People's Armed Police Forces , Logistics University of Chinese People's Armed Police Forces , Tianjin , China.
  • Jiaqi Lin
    Department of Chemical Engineering and Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Shuhong Qing
    Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Clinical Research Center for Oral Diseases of Zhejiang Province, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Hangzhou, Zhejiang, China.
  • Dan Long
    Children Nutrition Research Center, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Child Neurodevelopment and Cognitive Disorders, Chongqing, China.
  • Xuepeng Chen
    Department of Orthodontics, Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Hangzhou, PR China. Electronic address: cxp1979@zju.edu.cn.
  • Weifang Zhang
    Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Clinical Research Center for Oral Diseases of Zhejiang Province, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Hangzhou, Zhejiang, China; Social Medicine and Health Affairs Administration, Zhejiang University, Hangzhou, Zhejiang, China. Electronic address: chzwf@zju.edu.cn.

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